Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 2,271 to 2,280 of 202,214 articles

Comprehensive multi-omics analysis identifies cancer subtypes and prognostic signatures of hepatitis B virus-associated hepatocellular carcinoma.

NPJ precision oncology Jun 4, 2026
Hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) is a heterogeneous malignancy with poor prognosis, necessitating refined classification and novel therapeutics. Leveraging multi-omics data, we utilized the MOVICS package to identify tw... read more 

Multiobjective optimization of natural convection and entropy generation in a porous wavy cavity with a heated star-shaped obstacle using a hybrid nanofluid.

Discover nano Jun 4, 2026
Convectional heat transfer continues to receive extensive research attention due to its importance in vital applications such as solar energy collectors and cooling of nuclear reactors. This research investigates natural convection and associated ent... read more 

War-related stress is associated with resting-state functional connectivity of cognitive control and sensory networks in children.

Scientific reports Jun 4, 2026
The ability to read fluently relies on a synchronization between cognitive and sensory abilities and their corresponding brain networks. Environmental stress is related to reduced cognitive control abilities (i.e., executive functions; EF) as well as... read more 

Machine learning (ML) in intraoperative neuromonitoring (IONM): proof of concept.

Spine deformity Jun 4, 2026
PURPOSE: Intraoperative neuromonitoring (IONM) improves safety during pediatric spinal deformity surgery by providing real-time neurophysiological assessment, enabling the earlier detection of neural compromise and the potential prevention of permane... read more 

Integrated CNN-LSTM-XGBoost hybrid model predicts shale oil seismic attributes and global oil price trends.

Scientific reports Jun 4, 2026
This study proposes a hybrid CNN-LSTM-XGBoost model that integrates shale oil seismic attributes with macroeconomic indicators to predict global oil prices. The model extracts spatial features from seismic volumes using 3D CNNs and captures temporal ... read more 

A predictive model for flow index performance of pit drip irrigation emitters using BP-PSO algorithm.

Scientific reports Jun 4, 2026
It is crucial to accurately obtain the flow index in designing and developing labyrinth drip irrigation emitters. This study designed a pit drip irrigation emitter based on plant biomimetic principles, and created training-testing datasets (160 data ... read more 

Patient-specific modeling identifies metabolic interventions for reversing glucose use reprogramming in alcohol-associated hepatitis.

Communications biology Jun 4, 2026
Alcoholic hepatitis (AH) is an acute form of alcohol-associated liver disease with very few treatment options. Recent studies highlighted liver metabolic reprogramming in AH as an indicator of severity. We aim at identifying new intervention points t... read more 

Label-free interferometry platform for drug response profiling of bioprinted tumor organoids at single-organoid resolution.

Nature protocols Jun 4, 2026
Organoids have become mainstay tools for drug discovery and personalized medicine. High-throughput imaging readouts for drug screening of tumor organoids are of particular interest as organoid-level quantification of responses provides insights into ... read more 

Three-dimensional inversion of gravity data using implicit neural representations and scientific machine learning.

Scientific reports Jun 4, 2026
Inversion of gravity data is an important method for investigating subsurface density variations relevant to mineral exploration, geothermal assessment, carbon storage, natural hydrogen, groundwater resources, and tectonic evolution. Here we present ... read more 

An interpretable ultrasound-based deep learning system for early breast cancer in a Chinese population.

Insights into imaging Jun 4, 2026
OBJECTIVES: Current deep learning models for early breast cancer lack interpretability and multimodal integration, limiting their clinical acceptance. This study aimed to develop and evaluate a deep learning system that automates breast ultrasound ev... read more 
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